More interesting regions: an efficient road segmentation method based on vanishing point

Yao Wang; Fangfa Fu; Fengchang Lai; Weizhe Xu; Jinxiang Wang

doi:10.1117/1.JEI.28.1.013006

14 January 2019 More interesting regions: an efficient road segmentation method based on vanishing point

Yao Wang, Fangfa Fu, Fengchang Lai, Weizhe Xu, Jinxiang Wang

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 1, 013006 (January 2019). https://doi.org/10.1117/1.JEI.28.1.013006

Abstract

Accurate road segmentation is imperative for any effective advanced driver assistance system. Existing state-of-the-art methods are generally based on network architectures; however, high accuracy comes at the expense of abundant annotated data and high-computational cost. The inherently large amount of nonroad areas increases the running time and affects road segmentation accuracy. We propose a method based on the data enhancement of vanishing points (VPs) and deep learning to segment road areas. The VP serves as the location prior to remove redundant areas (e.g., sky and tall buildings). Contour maps reduce noise voters from background interference to mitigate the low VP estimation speed. A voting strategy is deployed to further accelerate the processing speed. After VP presegmentation, the remaining regions are fed into a multilayered extended semantic network (MultiNet), which consists of VGG16 and FCN. By combining VPs and MultiNet, the proposed method reveals discriminative features for road segmentation better than the original MultiNet. The convergent speed of training the MultiNet model is 25% faster than the original network owing to the location priors. Experimental results reveal that the proposed method outperforms several other methods with regard to both efficiency and accuracy.

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Yao Wang, Fangfa Fu, Fengchang Lai, Weizhe Xu, and Jinxiang Wang "More interesting regions: an efficient road segmentation method based on vanishing point," Journal of Electronic Imaging 28(1), 013006 (14 January 2019). https://doi.org/10.1117/1.JEI.28.1.013006

Received: 24 July 2018; Accepted: 17 December 2018; Published: 14 January 2019

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